SQUEEZE

SQUEEZE is based on Markov Chain Monte-Carlo (MCMC) exploration of the imaging probability space, and reconstructs images and associated error bars from standard OIFITS data. SQUEEZE leverages the Open Multi-Processing (OpenMP) application programming interface to implement simulated annealing and parallel tempering, in the hope of avoiding the local minima better than classic gradient-based image reconstruction software.

SQUEEZE is developed by Pr Fabien Baron of Georgia State University and
distributed under an open source (GPL v3) license.
If you encounter bugs or if you have specific requests for additional features, models, or other
enhancements, please send an email to Fabien Baron (This email address is being protected from spambots. You need JavaScript enabled to view it.).

Supported data types:

System requirements

SQUEEZE is designed to be cross-platform compatible. It has been tested on several variants of GNU/Linux and on Mac OSX. Test on additional
platforms are most welcomed.

SQUEEZE makes use of OpenMP, which requires a compatible compiler (gcc, Intel compiler, etc.). Support for OpenMP is now available for the clang/LLVM compiler, but may not be currently available on your platform (e.g. Mac OSX).

Installation

To download SQUEEZE, you first need to have git installed on your machine.

This will configure and build both SQUEEZE's sublibraries, CFITSIO and RngStreams, then SQUEEZE itself.

Visualization

SQUEEZE includes several visualization tools for GDL and Python (requires Astropy).
With these you can:
* Follow monothread reconstructions as they go, seeing chi2 and regularizations evolve in real time.
* Follow multithreaded reconstruction as they go, checking for thread mixing for parallel tempering or for converge for simulated annealing.
* Analyze the full MCMC probability chain of a reconstruction.
* Plot the residuals of the reconstructions.